Related papers: Protein sequence design with deep generative models

Related papers

Protein Large Language Models: A Comprehensive Survey [71.65899614084853]
Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. This work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications.
arXiv Detail & Related papers (2025-02-21T19:22:10Z)
Computational Protein Science in the Era of Large Language Models (LLMs) [54.35488233989787]
Computational protein science is dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. Recently, Language Models (pLMs) have emerged as a milestone in AI due to their unprecedented language processing & generalization capability.
arXiv Detail & Related papers (2025-01-17T16:21:18Z)
Large Language Model is Secretly a Protein Sequence Optimizer [24.55348363931866]
We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence. We demonstrate large language models (LLMs), despite being trained on massive texts, are secretly protein sequences.
arXiv Detail & Related papers (2025-01-16T03:44:16Z)
ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design [61.19456204667385]
We introduce ProteinWeaver, a two-stage framework for protein backbone design. ProteinWeaver generates high-quality, novel protein backbones through versatile domain assembly. By introducing a divide-and-assembly' paradigm, ProteinWeaver advances protein engineering and opens new avenues for functional protein design.
arXiv Detail & Related papers (2024-11-08T08:10:49Z)
Boosting Protein Language Models with Negative Sample Mining [20.721167029530168]
We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process for correlating the over-reliance on co-evolution knowledge. By capitalizing on this novel approach, our technique steers the training of transformer-based models within the attention score space.
arXiv Detail & Related papers (2024-05-28T07:24:20Z)
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering [24.415612744612773]
Proteins are essential to life's processes, underpinning evolution and diversity. Advances in sequencing technology have revealed millions of proteins, underscoring the need for sophisticated pre-trained protein models for biological analysis and AI development. Facebook's ESM2, the most advanced protein language model to date, leverages a masked prediction task for unsupervised learning, crafting amino acid representations with notable biochemical accuracy. Yet, it lacks in delivering functional protein insights, signaling an opportunity for enhancing representation quality. This study addresses this gap by incorporating protein family classification into ESM2's training, while a contextual prediction task fine-tunes local
arXiv Detail & Related papers (2024-04-24T11:09:43Z)
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions [58.819567030843025]
This paper extends data augmentation techniques previously used for images and texts to proteins and then benchmarks these techniques on a variety of protein-related tasks. We propose two novel semantic-level protein augmentation methods, namely Integrated Gradients Substitution and Back Translation Substitution. Finally, we integrate extended and proposed augmentations into an augmentation pool and propose a simple but effective framework, namely Automated Protein Augmentation (APA)
arXiv Detail & Related papers (2024-03-01T07:58:29Z)
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein [76.18058946124111]
We propose a unified protein language model, xTrimoPGLM, to address protein understanding and generation tasks simultaneously. xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. It can also generate de novo protein sequences following the principles of natural ones, and can perform programmable generation after supervised fine-tuning.
arXiv Detail & Related papers (2024-01-11T15:03:17Z)
Protein Sequence Design with Batch Bayesian Optimisation [0.0]
Protein sequence design is a challenging problem in protein engineering, which aims to discover novel proteins with useful biological functions. directed evolution is a widely-used approach for protein sequence design, which mimics the evolution cycle in a laboratory environment and conducts an iterative protocol. We propose a new method based on Batch Bayesian Optimization (Batch BO), a well-established optimization method, for protein sequence design.
arXiv Detail & Related papers (2023-03-18T14:53:20Z)
Structure-informed Language Models Are Protein Designers [69.70134899296912]
We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
arXiv Detail & Related papers (2023-02-03T10:49:52Z)
Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
arXiv Detail & Related papers (2022-05-20T19:38:00Z)
Structure-aware Protein Self-supervised Learning [50.04673179816619]
We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
arXiv Detail & Related papers (2022-04-06T02:18:41Z)
Using Genetic Programming to Predict and Optimize Protein Function [65.25258357832584]
We propose POET, a computational Genetic Programming tool based on evolutionary methods to enhance screening and mutagenesis in Directed Evolution. As a proof-of-concept we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer mechanism. Our results indicate that a computational modelling tool like POET can help to find peptides with 400% better functionality than used before.
arXiv Detail & Related papers (2022-02-08T18:08:08Z)
Deep Generative Modeling for Protein Design [0.0]
Deep learning approaches have produced breakthroughs in fields such as image classification and natural language processing. generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. We discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.
arXiv Detail & Related papers (2021-08-31T14:38:26Z)

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